Identifying and handling label errors can significantly enhance the accuracy of supervised machine learning models. Recent approaches for identifying label errors demonstrate that a low self-confidence of models with respect to a certain label represents a good indicator of an erroneous label. However, latest work has built on softmax probabilities to measure self-confidence. In this paper, we argue that -- as softmax probabilities do not reflect a model's predictive uncertainty accurately -- label error detection…