  Uncertainty estimation of trained deep learning networks is valuable for
optimizing learning efficiency and evaluating the reliability of network
predictions. In this paper, we propose a method for estimating uncertainty in
deep learning image classification using test-time mixup augmentation (TTMA).
To improve the ability to distinguish correct and incorrect predictions in
existing aleatoric uncertainty, we introduce TTMA data uncertainty (TTMA-DU) by
applying mixup augmentation to test data and measuring the entropy of the
predicted label histogram. In addition to TTMA-DU, we propose TTMA
class-specific uncertainty (TTMA-CSU), which captures aleatoric uncertainty
specific to individual classes and provides insight into class confusion and
class similarity within the trained network. We validate our proposed methods
on the ISIC-18 skin lesion diagnosis dataset and the CIFAR-100 real-world image
classification dataset. Our experiments show that (1) TTMA-DU more effectively
differentiates correct and incorrect predictions compared to existing
uncertainty measures due to mixup perturbation, and (2) TTMA-CSU provides
information on class confusion and class similarity for both datasets.
