Inverse protein folding generates valid amino acid sequences that can fold into a desired protein structure, with recent deep learning advances showing strong potential and competitive performance. However, challenges remain, such as predicting elements with high structural uncertainty, including disordered regions. To tackle such low-confidence residue prediction, we propose a mask-prior-guided denoising diffusion (MapDiff) framework that accurately captures both structural information and residue interactions for inverse protein folding. MapDiff is a discrete diffusion probabilistic model that iteratively generates amino acid sequences with reduced noise, conditioned on a given protein backbone. To incorporate structural information and residue interactions, we have developed a graph-based denoising network with a mask-prior pretraining strategy. Moreover, in the generative process, we combine the denoising diffusion implicit model with Monte-Carlo dropout to reduce uncertainty. Evaluation on four challenging sequence design benchmarks shows that MapDiff substantially outperforms state-of-the-art methods. Furthermore, the in silico sequences generated by MapDiff closely resemble the physico-chemical and structural characteristics of native proteins across different protein families and architectures. Bai and colleagues present MapDiff, a discrete diffusion-based framework for generating amino acid sequences conditioned on a target protein structure, with strong performance in predicting uncertain regions and achieving high in silico foldability.