  Humans exhibit disagreement during data labeling. We term this disagreement
as human label uncertainty. In this work, we study the ramifications of human
label uncertainty (HLU). Our evaluation of existing uncertainty estimation
algorithms, with the presence of HLU, indicates the limitations of existing
uncertainty metrics and algorithms themselves in response to HLU. Meanwhile, we
observe undue effects in predictive uncertainty and generalizability. To
mitigate the undue effects, we introduce a novel natural scene statistics (NSS)
based label dilution training scheme without requiring massive human labels.
Specifically, we first select a subset of samples with low perceptual quality
ranked by statistical regularities of images. We then assign separate labels to
each sample in this subset to obtain a training set with diluted labels. Our
experiments and analysis demonstrate that training with NSS-based label
dilution alleviates the undue effects caused by HLU.
