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Medical image segmentation takes an important position in various clinical applications. Deep learning has emerged as the predominant solution for automated segmentation of volumetric medical images. 2.5D-based segmentation models bridge computational efficiency of 2D-based models and spatial perception capabilities of 3D-based models. However, prevailing 2.5D-based models often treat each slice equally, failing to effectively learn and exploit inter-slice information, resulting in suboptimal segmentation performances. In this paper, a novel Momentum encoder-based inter-slice fusion transformer (MOSformer) is proposed to overcome this issue by leveraging inter-slice information at multi-scale feature maps extracted by different encoders. Specifically, dual encoders are employed to enhance feature distinguishability among different slices. One of the encoders is moving-averaged to maintain the consistency of slice representations. Moreover, an IF-Swin transformer module is developed to fuse inter-slice multi-scale features. The MOSformer is evaluated on three benchmark datasets (Synapse, ACDC, and AMOS), establishing a new state-of-the-art with 85.63%, 92.19%, and 85.43% of DSC, respectively. These promising results indicate its competitiveness in medical image segmentation. Codes and models of MOSformer will be made publicly available upon acceptance., Comment: Under Review