Accurate segmentation of myocardial pathology, including scar and edema, from magnetic resonance images (MRI) is of significant clinical importance for diagnosing, planning treatment, and assessing prognosis in cardiovascular diseases. Multi-sequence cardiac magnetic resonance images provide additional information for pathological segmentation, while ventricular blood pool regions offer positional anatomical priors for identifying scar and edema. Therefore, we propose a multi-task framework based on SimAM-UNet, where SimAM generates feature weights to better focus on modality-specific features for the segmentation of myocardial pathology using multi-sequence cardiac magnetic resonance images. The first task involves segmenting and extracting features of the myocardium and blood pool, which are relatively easy to obtain. The second task focuses on segmenting scars and edema by integrating positional feature constraints derived from the myocardium and blood pool. To address the multi-center data problem encountered in real-world data, we incorporate a data augmentation pipeline that simulates the generation of data samples from different centers, enhancing the domain generalization of our method. Our proposed method was evaluated on the MyoPS++ track of the CARE2024 Challenge, and the experimental results demonstrated the effectiveness of our multi-task framework for myocardial pathological segmentation.