diff --git a/monai/losses/dice.py b/monai/losses/dice.py
index 5561012f0e..5f8421f384 100644
--- a/monai/losses/dice.py
+++ b/monai/losses/dice.py
@@ -213,7 +213,7 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         f: torch.Tensor = 1 - numerator / denominator
 
         num_of_classes = target.shape[1]
-        if self.class_weight is not None and num_of_classes != 1:
+        if self.class_weight is not None and n_pred_ch != 1:
             # make sure the lengths of weights are equal to the number of classes
             if self.class_weight.ndim == 0:
                 self.class_weight = torch.as_tensor([self.class_weight] * num_of_classes)
diff --git a/tests/losses/test_dice_loss.py b/tests/losses/test_dice_loss.py
index d8fb5e1195..32b6ba6db0 100644
--- a/tests/losses/test_dice_loss.py
+++ b/tests/losses/test_dice_loss.py
@@ -184,6 +184,13 @@ def test_shape(self, input_param, input_data, expected_val):
         result = DiceLoss(**input_param).forward(**input_data)
         np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-5)
 
+    @parameterized.expand([([0], 0.0), ([2], 0.999995)])
+    def test_single_foreground_weight(self, weight, expected_val):
+        prediction = torch.full((1, 2, 1, 2), 0.5)
+        target = torch.tensor([[[[1.0, 0.0]], [[0.0, 1.0]]]])
+        result = DiceLoss(include_background=False, weight=weight)(prediction, target)
+        np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-5)
+
     def test_ill_shape(self):
         loss = DiceLoss()
         with self.assertRaisesRegex(AssertionError, ""):