Saliency prediction plays a critical role in understanding visual attention as it is a cornerstone for both natural scene understanding and automated document analysis. In this work, we propose the UNETRSal model for saliency prediction. Based on UNETR transformer-based model, we introduce a new decoder to increase efficiency on 2D images. Comprehensive evaluations on benchmark datasets, such as SALICON and CAT2000, demonstrate that UNETRSal achieves state-of-the-art performance across multiple saliency metrics, surpassing both conventional CNN-based and transformer-based methods. These results not only underscore the strengths of hybrid transformer architectures in modeling visual attention but also highlight the potential impact on advancing document representation modeling and layout analysis.