  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 requires more
sophisticated measures of model uncertainty. Therefore, we develop a range of
novel, model-agnostic algorithms for Uncertainty Quantification-Based Label
Error Detection (UQ-LED), which combine the techniques of confident learning
(CL), Monte Carlo Dropout (MCD), model uncertainty measures (e.g., entropy),
and ensemble learning to enhance label error detection. We comprehensively
evaluate our algorithms on four image classification benchmark datasets in two
stages. In the first stage, we demonstrate that our UQ-LED algorithms
outperform state-of-the-art confident learning in identifying label errors. In
the second stage, we show that removing all identified errors from the training
data based on our approach results in higher accuracies than training on all
available labeled data. Importantly, besides our contributions to the detection
of label errors, we particularly propose a novel approach to generate
realistic, class-dependent label errors synthetically. Overall, our study
demonstrates that selectively cleaning datasets with UQ-LED algorithms leads to
more accurate classifications than using larger, noisier datasets.
