BACKGROUND: Accurate segmentation of lesions is beneficial for quantitative analysis and precision medicine in multimodal magnetic resonance imaging (MRI). PURPOSE: Currently, multimodal MRI fusion segmentation networks still face two main issues. On one hand, simple feature concatenation fails to fully capture the complex relationships between different modalities, as it overlooks the importance of dynamically changing feature weights across modalities. On the other hand, the unlearnable nature of upsampling in segmentation networks leads to feature misalignment issues during feature aggregation with the decoder, resulting in spatial misalignments between feature maps of different levels and ultimately pixel-level classification errors in predictions. METHODS: This paper introduces the Self-adaptive weighted fusion and Self-adaptive aligned Network (S RESULTS: This paper conducts experiments on two MRI datasets: ISLES 2022 and BraTS 2020. In the ISLES 2022 dataset, compared to the sub-optimal network MedNeXt, the proposed S CONCLUSIONS: Experimental results demonstrate that S