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Few-shot segmentation aims to achieve object-specific semantic segmentation in query images by conditioning on specific objects identified within support images. Previous methodologies predominantly relied on evaluating similarities between semantic-level prototypes of support features or comprehensive pixelwise support information and query images to inform predictions for queries. However, a significant domain discrepancy has been identified between the extracted query features and their support counterparts, resulting in a significant misalignment in the distribution of support and query features. As a result, direct analogies between support and query information cannot ensure the precision of query predictions. In this investigation, a distribution adaptation network is introduced, meticulously designed to learn discriminative representations and alleviate the distributional disparities between support features and query features. The adaptation network embeds both support and query features into Reproducing Kernel Hilbert Spaces (RKHS) and optimally aligns their distributions. The distributional gap is effectively bridged through optimal kernel embedding alignment, facilitated by a multiple kernel learning strategy. Extensive experimentation on datasets PASCAL-5i, COCO-20i and FSS-1000 unequivocally demonstrates that the proposed Kernel-Aligned Distribution Network (KADN) achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]