## 1 Introduction  
Encoder-decoder network architecture has emerged as a dominant design in medical image segmentation, starting with U-Net, a fully symmetric variant [1]. The encoder in this architecture primarily focuses on extracting pixel location features via convolutional operations and downsampling. The decoder, conversely, reinstates spatial dimensions and pixel location information using deconvolutional operations. The copy-and-crop connection that exists between encoder and decoder layers facilitates the seamless transfer of multiscale semantic information. With this advanced framework, U-Net has been successfully deployed for segmenting a variety of regions within the human body, proving its efficacy across CT [2; 3; 4], ultrasound [5; 6; 7; 8], and MRI modalities [9; 10; 11]. The segmentation capabilities of U-Net were later expanded upon by [PERSON], who introduced a 3D version capable of achieving volumetric segmentation through the extraction of sparsely annotated volumetric images [12]. Another significant advancement was made by [PERSON], who introduced an attention gate model. This model enabled CNNs to automatically learn the structures of targets with diverse shapes and sizes [11]. The resultant attention U-Net-based model demonstrated superior sensitivity and accuracy while minimizing computational overhead. [PERSON] also contributed to the development of the model by proposing a high-resolution Dense-UNet by comparing the performance of 2D and 3D convolutional operations within residual networks and densely connected networks, respectively [13]. Ongoing research related to various U-Net architectures such as residual networks, inception networks, densely connected networks, and feature normalization continues to advance the field [2; 14; 15; 16]. The CNN-based U-Net has been ubiquitous in its application, which has led to the creation of a family of CNN-based U-Nets [2; 3; 13; 14; 15; 17; 18; 19]. Vision Transformers (ViT) have garnered considerable attention as an influential architecture for diverse computer vision tasks, surpassing traditional CNNs in many instances [20]. Distinct from CNNs, which employ local convolutions to capture spatial information, ViTs utilize self-attention mechanisms [21] to simultaneously process both local and global contextual information. This attribute enables ViT networks to effectively capture intricate patterns and details, rendering them particularly suitable for medical image segmentation tasks that require a sufficient accurate delineation of complex structures [9; 22; 23; 24; 25]. Current ViT-improved segmentation networks in the literature are largely designed around a U-Net style architecture with self-attention layers. TransUNet [9] was introduced to address the bottleneck of UNet with ViT layers when processing high-level feature information. SwinUNet [22] was designed to utilize a promising shift-window-modified ViT [26] to construct a pure ViT-based UNet-style segmentation network. UNETR [23] introduced explored the ViT-based layer to tackle 3D medical semantic segmentation tasks. The remarkable performance of both ViT- and CNN-based networks is possible mainly due to the availability of large volumes of high-quality data annotations. This need also constitutes a barrier to deploying advanced machine learning networks to clinical contexts. Semisupervised learning (SSL) [6; 24; 27; 28], and weakly supervised learning (WSL) [29; 30; 31], which aim to tackle the expensive cost of labeling data, have been explored to ViT and CNN, but their application in medical image segmentation still remains relatively underexplored, particularly in the context of noisy labels. The term noisy labels refers to inaccuracies and inconsistencies in the annotated ground truth data, and can significantly challenge network training and generalization [17; 32; 33]. In medical image segmentation, noisy labels are often inevitable due to factors such as interobserver variability, lack of experience of junior clinicians, and the sheer intricate nature of anatomical structures.