In medical imaging, accurate image segmentation is essential for precise diagnosis and treatment planning. However, challenges such as image quality variations, complex anatomical structures, and the impracticality of traditional segmentation methods require extensive, source-specific feature extraction. In this study, we introduce DualPath-FFNet, a novel deep-learning architecture for medical image segmentation. The architecture utilizes DenseNet201 and Efficient- NetV2 pre-trained networks as encoders to extract diverse feature representations from input images. Two decoders generate intermediate outputs, refined with the original input image and further enhanced by skip connections. A Sigmoid gated attention (SGA) module dynamically fuses features, prioritizing task-specific information. A third encoder aggregates information from previous stages, integrating skip connections and utilizing the SGA module. This dynamic fusion, combined with multi-view encoding and skip connections, promotes robust and informative representation learning. The performance of this model was comparatively evaluated using dice coefficient and Intersection over Union metrics over 5 different types of medical image data set against three other relavant models. DualPath-FF Net achieves an average Dice score of 0.914 across multiple datasets, demonstrating robust performance and generalizability.