Multi-modal MR imaging plays a crucial role in clinical diagnosis and medical research. However, its widespread adoption is hindered by significant time and hardware costs. Medical image translation, which aims to synthesize missing modalities from available data, presents a promising solution. Nevertheless, existing models often struggle to maintain the structural consistency required for clinical applications. We introduced a disentanglement diffusion network –DisDiff, a novel disentangled adversarial diffusion framework designed to address these challenges. DisDiff incorporates a Disentangled module that decouples content and style factors within image features, thereby enabling the generation of anatomically precise images. Conditioned on disentangled representations, compared to traditional diffusion-based models, DisDiff not only accelerates the learning process, but also improves image quality and enhances training efficiency. In addition, we proposed a content discriminator module to further enforce anatomical consistency, effectively addressing the lack of explicit structural guidance in conventional diffusion models. Experimental evaluations on multi-contrast MRI translation demonstrate that DisDiff substantially outperforms existing methods in both image quality and structural preservation, positioning it as a promising solution for real-world clinical applications.