This paper introduces PIXIE (Penalised Pixel Intersection Error), a novel loss function aimed at improving model performance through the penalization of error pixels and alignment of the predicted mask with the ground truth mask. We explore the effectiveness of PIXIE alongside U-Net and DeepLabV3 networks. In addition to novel loss functions, this paper conducts model selection studies to understand overestimations and underestimations. We conduct a comprehensive evaluation using diverse datasets spanning medical imaging (breast tumor, COVID-19, Brain MRI) and remote sensing domains (forest fire, water bodies satellite images). Performance metrics, such as Intersection over Union (IoU), Dice Coefficient (DC), Area Error Ratio (AER), precision, and recall are quantitatively assessed. Moreover, we compare PIXIE with traditional loss functions like Jacard loss, Focal loss, and Binary crossentropy. PIXIE demonstrates comparable performance to the traditional loss functions in certain metrics and outperforms them in others, establishing itself as a leading approach in achieving exceptional results across all examined segmentation datasets. These findings represent a substantial contribution to the practical evolution of semantic segmentation in computer vision, offering essential insights into the optimization of loss functions for the development of accurate and robust models.