  Optic disc and cup segmentation plays a crucial role in automating the
screening and diagnosis of optic glaucoma. While data-driven convolutional
neural networks (CNNs) show promise in this area, the inherent ambiguity of
segmenting objects and background boundaries in the task of optic disc and cup
segmentation leads to noisy annotations that impact model performance. To
address this, we propose an innovative label-denoising method of Multiple
Pseudo-labels Noise-aware Network (MPNN) for accurate optic disc and cup
segmentation. Specifically, the Multiple Pseudo-labels Generation and Guided
Denoising (MPGGD) module generates pseudo-labels by multiple different
initialization networks trained on true labels, and the pixel-level consensus
information extracted from these pseudo-labels guides to differentiate clean
pixels from noisy pixels. The training framework of the MPNN is constructed by
a teacher-student architecture to learn segmentation from clean pixels and
noisy pixels. Particularly, such a framework adeptly leverages (i) reliable and
fundamental insight from clean pixels and (ii) the supplementary knowledge
within noisy pixels via multiple perturbation-based unsupervised consistency.
Compared to other label-denoising methods, comprehensive experimental results
on the RIGA dataset demonstrate our method's excellent performance. The code is
available at https://github.com/wwwtttjjj/MPNN
