  Learning segmentation from noisy labels is an important task for medical
image analysis due to the difficulty in acquiring highquality annotations. Most
existing methods neglect the pixel correlation and structural prior in
segmentation, often producing noisy predictions around object boundaries. To
address this, we adopt a superpixel representation and develop a robust
iterative learning strategy that combines noise-aware training of segmentation
network and noisy label refinement, both guided by the superpixels. This design
enables us to exploit the structural constraints in segmentation labels and
effectively mitigate the impact of label noise in learning. Experiments on two
benchmarks show that our method outperforms recent state-of-the-art approaches,
and achieves superior robustness in a wide range of label noises. Code is
available at https://github.com/gaozhitong/SP_guided_Noisy_Label_Seg.
