Existing natural image matting algorithms inevitably have flaws in their predictions on difficult cases, and their one-step prediction manner cannot further correct these errors. In this paper, we investigate a multi-step iterative approach for the first time to tackle the challenging natural image matting task, and achieve excellent performance by introducing a pixel-level denoising diffusion method (DiffMatte) for the alpha matte refinement. To improve iteration efficiency, we design a lightweight diffusion decoder as the only iterative component to directly denoise the alpha matte, saving the huge computational overhead of repeatedly encoding matting features. We also propose an ameliorated self-aligned strategy to consolidate the performance gains brought about by the iterative diffusion process. This allows the model to adapt to various types of errors by aligning the noisy samples used in training and inference, mitigating performance degradation caused by sampling drift. Extensive experimental results demonstrate that DiffMatte not only reaches the state-of-the-art level on the mainstream Composition-1k test set, surpassing the previous best methods by 8% and 15 in the SAD metric and MSE metric respectively, but also show stronger generalization ability in other benchmarks. The code will be open-sourced for the following research and applications.