  Learning against label noise is a vital topic to guarantee a reliable
performance for deep neural networks. Recent research usually refers to dynamic
noise modeling with model output probabilities and loss values, and then
separates clean and noisy samples. These methods have gained notable success.
However, unlike cherry-picked data, existing approaches often cannot perform
well when facing imbalanced datasets, a common scenario in the real world. We
thoroughly investigate this phenomenon and point out two major issues that
hinder the performance, i.e., \emph{inter-class loss distribution discrepancy}
and \emph{misleading predictions due to uncertainty}. The first issue is that
existing methods often perform class-agnostic noise modeling. However, loss
distributions show a significant discrepancy among classes under class
imbalance, and class-agnostic noise modeling can easily get confused with noisy
samples and samples in minority classes. The second issue refers to that models
may output misleading predictions due to epistemic uncertainty and aleatoric
uncertainty, thus existing methods that rely solely on the output probabilities
may fail to distinguish confident samples. Inspired by our observations, we
propose an Uncertainty-aware Label Correction framework~(ULC) to handle label
noise on imbalanced datasets. First, we perform epistemic uncertainty-aware
class-specific noise modeling to identify trustworthy clean samples and
refine/discard highly confident true/corrupted labels. Then, we introduce
aleatoric uncertainty in the subsequent learning process to prevent noise
accumulation in the label noise modeling process. We conduct experiments on
several synthetic and real-world datasets. The results demonstrate the
effectiveness of the proposed method, especially on imbalanced datasets.
