Lesion segmentation and classification in breast ultrasound inform each other, yet many multi-task networks couple the two tasks only through shared features. This paper introduces a decoder in which a Task Interaction Module (TIM) exchanges latent features between the segmentation and classification branches at each of four levels, and an Adaptive Interaction Weighting (AIW) unit uses activation dispersion to set, per image and level, one softmax budget that the two directions share. Multi-scale context fusion (MSCF) adds dilated context to the residual blocks. On BUSI and BUSI-WHU, split so that duplicate images and, for BUSI-WHU, images of one patient stay within one partition, the model exceeds the mean IoU of CNN, transformer, multi-task and same-backbone baselines, reaching 72.9% and 85.4%, and flags lesion pixels in 19.0% of its predictions on normal BUSI test images against 33.3% for MTI-Net. Replacing the per-image budget by fixed or learned constant coefficients, or by independent sigmoid gates, costs 0.92 to 1.51 IoU points on BUSI. Code and data partitions: https://github.com/C-loud-Nine/Adaptive-Task-Interaction-BUS

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