Few-shot medical image segmentation methods typically assume a single ground-truth annotation, overlooking systematic variability across expert raters commonly observed in clinical datasets. We propose an attention-based prototype calibration framework for few-shot multi-rater segmentation that models rater-specific deviations from a consensus representation in prototype space. A lightweight yet principled attention operator directly refines rater prototypes without modifying the backbone feature extractor, making the approach fully compatible with existing prototype-based few-shot segmentation methods. This design preserves semantic consistency while enabling personalized segmentation outputs with minimal computational overhead. Experiments on multi-rater medical imaging datasets demonstrate consistent improvements over baseline prototype approaches, highlighting the effectiveness of structured prototype calibration for modeling annotation variability. Our code is available at https://github.com/truong2710-cyber/JAPC.

翻译：少样本医学图像分割方法通常假设存在单个真实标注，忽视了临床数据集中常见的不同专家标注者之间的系统性差异。我们提出了一种基于注意力机制的原型校准框架，用于少样本多标注者分割任务，该框架在原型空间中建模标注者与共识表示之间的特异性偏差。一个轻量级且原理清晰的注意力算子可直接优化标注者原型，无需修改主干特征提取器，从而使其能够与现有基于原型的少样本分割方法完全兼容。这一设计在保持语义一致性的同时，以极小的计算开销实现个性化分割输出。在多标注者医学影像数据集上的实验表明，与基准原型方法相比，该方法持续获得改进，凸显了结构化原型校准在建模标注变异方面的有效性。我们的代码开源在 https://github.com/truong2710-cyber/JAPC。