  Deep neural networks (DNNs) achieve promising performance in visual
recognition under the independent and identically distributed (IID) hypothesis.
In contrast, the IID hypothesis is not universally guaranteed in numerous
real-world applications, especially in medical image analysis. Medical image
segmentation is typically formulated as a pixel-wise classification task in
which each pixel is classified into a category. However, this formulation
ignores the hard-to-classified pixels, e.g., some pixels near the boundary
area, as they usually confuse DNNs. In this paper, we first explore that
hard-to-classified pixels are associated with high uncertainty. Based on this,
we propose a novel framework that utilizes uncertainty estimation to highlight
hard-to-classified pixels for DNNs, thereby improving its generalization. We
evaluate our method on two popular benchmarks: prostate and fundus datasets.
The results of the experiment demonstrate that our method outperforms
state-of-the-art methods.
