  Deep learning-based edge detectors heavily rely on pixel-wise labels which
are often provided by multiple annotators. Existing methods fuse multiple
annotations using a simple voting process, ignoring the inherent ambiguity of
edges and labeling bias of annotators. In this paper, we propose a novel
uncertainty-aware edge detector (UAED), which employs uncertainty to
investigate the subjectivity and ambiguity of diverse annotations.
Specifically, we first convert the deterministic label space into a learnable
Gaussian distribution, whose variance measures the degree of ambiguity among
different annotations. Then we regard the learned variance as the estimated
uncertainty of the predicted edge maps, and pixels with higher uncertainty are
likely to be hard samples for edge detection. Therefore we design an adaptive
weighting loss to emphasize the learning from those pixels with high
uncertainty, which helps the network to gradually concentrate on the important
pixels. UAED can be combined with various encoder-decoder backbones, and the
extensive experiments demonstrate that UAED achieves superior performance
consistently across multiple edge detection benchmarks. The source code is
available at \url{https://github.com/ZhouCX117/UAED}
