In the field of multi-organ 3D medical image segmentation, Convolutional Neural Networks (CNNs) are limited to extracting local feature information, while Transformer-based architectures suffer from high computational complexity and inadequate extraction of spatial and channel layer information. Moreover, the large number and varying sizes of organs to be segmented result in suboptimal model robustness and segmentation outcomes. To address these challenges, this paper introduces a novel network architecture, DS-UNETR++, specifically designed for 3D medical image segmentation. The proposed network features a dual-branch feature encoding mechanism that categorizes images into coarse-grained and fine-grained types before processing them through the encoding blocks. Each encoding block comprises a downsampling layer and a Gated Shared Weighted Pairwise Attention (G-SWPA) submodule, which dynamically adjusts the influence of spatial and channel attention on feature extraction. Additionally, a Gated Dual-Scale Cross-Attention Module (G-DSCAM) is incorporated at the bottleneck stage. This module employs dimensionality reduction techniques to cross-coarse-grained and fine-grained features, using a gating mechanism to dynamically balance the ratio of these two types of feature information, thereby achieving effective multi-scale feature fusion. Finally, comprehensive evaluations were conducted on four public medical datasets. Experimental results demonstrate that DS-UNETR++ achieves good segmentation performance, highlighting the effectiveness and significance of the proposed method and offering new insights for various organ segmentation tasks.