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This application discloses a multimodal medical image segmentation method and device with dynamic gating adaptive feature fusion, belonging to the field of image segmentation technology. The method includes: constructing a dynamic gating adaptive feature fusion architecture; the dynamic gating adaptive feature fusion architecture includes: a stochastic modality missing simulator and a medical image segmentation network connected sequentially; the medical image segmentation network includes: an independent feature encoding module, a quality-aware scoring sub-network, a dynamic gating controller, and a decoder connected sequentially; using a historical medical image dataset and the model's total loss function, the dynamic gating adaptive feature fusion architecture is trained using a five-fold cross-validation experiment, and the trained medical image segmentation network is determined as the segmentation model for multimodal medical image segmentation of target areas. This application achieves high robustness segmentation performance for modality missing and quality-impaired scenes by constructing a dynamic gating adaptive feature fusion architecture.