diff --git a/monai/metrics/rocauc.py b/monai/metrics/rocauc.py
index c36bb507c9..73f15534a9 100644
--- a/monai/metrics/rocauc.py
+++ b/monai/metrics/rocauc.py
@@ -66,9 +66,14 @@ def aggregate(self):  # type: ignore
 def _calculate(y_pred: torch.Tensor, y: torch.Tensor) -> float:
     if not (y.ndimension() == y_pred.ndimension() == 1 and len(y) == len(y_pred)):
         raise AssertionError("y and y_pred must be 1 dimension data with same length.")
-    if not y.unique().equal(torch.tensor([0, 1], dtype=y.dtype, device=y.device)):
-        warnings.warn("y values must be 0 or 1, can not be all 0 or all 1, skip AUC computation and return `Nan`.")
+    y_unique = y.unique()
+    if len(y_unique) == 1:
+        warnings.warn(f"y values can not be all {y_unique.item()}, skip AUC computation and return `Nan`.")
         return float("nan")
+    if not y_unique.equal(torch.tensor([0, 1], dtype=y.dtype, device=y.device)):
+        warnings.warn(f"y values must be 0 or 1, but in {y_unique.tolist()}, skip AUC computation and return `Nan`.")
+        return float("nan")
+
     n = len(y)
     indices = y_pred.argsort()
     y = y[indices].cpu().numpy()
diff --git a/tests/test_compute_roc_auc.py b/tests/test_compute_roc_auc.py
index 592bf9459c..887db08c7c 100644
--- a/tests/test_compute_roc_auc.py
+++ b/tests/test_compute_roc_auc.py
@@ -77,10 +77,39 @@
     float("nan"),
 ]
 
+TEST_CASE_9 = [
+    torch.tensor([[0.1, 0.9], [0.3, 1.4], [0.2, 0.1], [0.1, 0.5]]),
+    torch.tensor([[1], [1], [1], [1]]),
+    True,
+    2,
+    "macro",
+    float("nan"),
+]
+
+TEST_CASE_10 = [
+    torch.tensor([[0.1, 0.9], [0.3, 1.4], [0.2, 0.1], [0.1, 0.5]]),
+    torch.tensor([[0, 0], [1, 1], [2, 2], [3, 3]]),
+    True,
+    None,
+    "macro",
+    float("nan"),
+]
+
 
 class TestComputeROCAUC(unittest.TestCase):
     @parameterized.expand(
-        [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7, TEST_CASE_8]
+        [
+            TEST_CASE_1,
+            TEST_CASE_2,
+            TEST_CASE_3,
+            TEST_CASE_4,
+            TEST_CASE_5,
+            TEST_CASE_6,
+            TEST_CASE_7,
+            TEST_CASE_8,
+            TEST_CASE_9,
+            TEST_CASE_10,
+        ]
     )
     def test_value(self, y_pred, y, softmax, to_onehot, average, expected_value):
         y_pred_trans = Compose([ToTensor(), Activations(softmax=softmax)])
@@ -91,7 +120,18 @@ def test_value(self, y_pred, y, softmax, to_onehot, average, expected_value):
         np.testing.assert_allclose(expected_value, result, rtol=1e-5)
 
     @parameterized.expand(
-        [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7, TEST_CASE_8]
+        [
+            TEST_CASE_1,
+            TEST_CASE_2,
+            TEST_CASE_3,
+            TEST_CASE_4,
+            TEST_CASE_5,
+            TEST_CASE_6,
+            TEST_CASE_7,
+            TEST_CASE_8,
+            TEST_CASE_9,
+            TEST_CASE_10,
+        ]
     )
     def test_class_value(self, y_pred, y, softmax, to_onehot, average, expected_value):
         y_pred_trans = Compose([ToTensor(), Activations(softmax=softmax)])