Deep learning has achieved remarkable accuracy in medical image segmentation, particularly for larger structures with well-defined boundaries. However, its effectiveness can be challenged by factors such as irregular object shapes and edges, non-smooth surfaces, small target areas, etc. which complicate the ability of networks to grasp the intricate and diverse nature of anatomical regions. In response to these challenges, we propose an to integrate object boundary smoothness and size into account, with the goal to improve segmentation performance in intricate anatomical regions. In this regard, we introduce two parameters derived from surface smoothness and volume information and integrate them into focal loss. Our proposed loss function dynamically adjusts itself based on an object's surface smoothness, and size, based on the ratio of targeted area and background. We evaluated the performance of the proposed loss function on the PICAI 2022 and BraTS 2018 datasets. In the PICAI 2022 dataset, the it achieved an Intersection over Union (IoU) score of 0.696 and a Dice Similarity Coefficient (DSC) of 0.769, outperforming the regular focal Loss (FL) by 5.5\% and 5.4\% respectively. It also surpassed the best baseline by 2.0\% and 1.2\%. In the BraTS 2018 dataset, it achieved an IoU score of 0.883 and a DSC score of 0.931. Our ablation experiments also show that the proposed integration strategy surpasses conventional losses (this includes Dice Loss, Focal Loss, and their hybrid variants) by large margin in IoU, DSC, and other metrics.