Medical image segmentation plays a pivotal role in computer-aided diagnosis and treatment planning. Traditional segmentation approaches often struggle to balance global and local context, either capturing overall anatomical structures or focusing on minute details, but not both. This paper introduces the Swin-Hierarchical Attention Unet (Swin-HAUnet), which harmonizes this dichotomy by integrating global contextual insights with local feature enhancement. The proposed network architecture employs a hybrid approach, leveraging an advanced transformer-based encoder to process wide-ranging contextual information and an attention-enhanced decoder to refine the segmentation of nuanced and intricate anatomical features. We performed experiments on two publicly available datasets, the Synapse multi-organ segmentation CT dataset and the UW-Madison dataset. The Swin-HAUnet shows a marked improvement in performance, achieving a Dice similarity coefficient of 79.91%, a notable increase of 1.26% over the baseline model on Synapse datasets. These results underscore the model’s effectiveness in complex segmentation tasks and the importance of attention mechanisms in medical image analysis.