  Collecting annotations from multiple independent sources could mitigate the
impact of potential noises and biases from a single source, which is a common
practice in medical image segmentation. Learning segmentation networks from
multi-source annotations remains a challenge due to the uncertainties brought
by the variance of annotations and the quality of images. In this paper, we
propose an Uncertainty-guided Multi-source Annotation Network (UMA-Net), which
guides the training process by uncertainty estimation at both the pixel and the
image levels. First, we developed the annotation uncertainty estimation module
(AUEM) to learn the pixel-wise uncertainty of each annotation, which then
guided the network to learn from reliable pixels by weighted segmentation loss.
Second, a quality assessment module (QAM) was proposed to assess the
image-level quality of the input samples based on the former assessed
annotation uncertainties. Importantly, we introduced an auxiliary predictor to
learn from the low-quality samples instead of discarding them, which ensured
the preservation of their representation knowledge in the backbone without
directly accumulating errors within the primary predictor. Extensive
experiments demonstrated the effectiveness and feasibility of our proposed
UMA-Net on various datasets, including 2D chest X-ray segmentation, fundus
image segmentation, and 3D breast DCE-MRI segmentation.
