Accurate brain tumor segmentation from MRI scans is critical for diagnosis and treatment planning. Despite the strong performance of recent deep learning approaches, two fundamental limitations remain: (1) the lack of reliable uncertainty quantification in single-model predictions, which is essential for clinical deployment because the level of uncertainty may impact treatment decision-making, and (2) the under-utilization of rich information in radiology reports that can guide segmentation in ambiguous regions. In this paper, we propose the Disagreement-Guided Refinement Network (DGRNet), a novel framework that addresses both limitations through multi-view disagreement-based uncertainty estimation and text-conditioned refinement. DGRNet generates diverse predictions via four lightweight view-specific adapters attached to a shared encoder-decoder, enabling efficient uncertainty quantification within a single forward pass. Afterward, we build disagreement maps to identify regions of high segmentation uncertainty, which are then selectively refined according to clinical reports. Moreover, we introduce a diversity-preserving training strategy that combines pairwise similarity penalties and gradient isolation to prevent view collapse. The experimental results on the TextBraTS dataset show that DGRNet favorably improves state-of-the-art segmentation accuracy by 2.4% and 11% in main metrics Dice and HD95, respectively, while providing meaningful uncertainty estimates.

翻译：从MRI扫描中准确分割脑肿瘤对于诊断和治疗计划至关重要。尽管近年深度学习方法性能强劲，但两个根本局限仍然存在：（1）单模型预测中缺乏可靠的不确定性量化，这对临床部署至关重要，因为不确定性水平可能影响治疗决策；（2）放射学报告中可用于指导模糊区域分割的丰富信息未被充分利用。本文提出分歧引导细化网络（DGRNet），一种通过多视角分歧不确定性估计和文本条件细化同时解决上述局限的新框架。DGRNet通过在共享编码器-解码器上附加四个轻量级视角适配器生成多样化预测，实现在单次前向传播中的高效不确定性量化。随后，我们构建分歧图以识别高分割不确定性区域，并根据临床报告对这些区域进行选择性细化。此外，我们引入一种多样性保持训练策略，结合成对相似性惩罚和梯度隔离以防止视角坍缩。在TextBraTS数据集上的实验结果表明，DGRNet在主要指标Dice和HD95上分别将最先进的分割准确率提升了2.4%和11%，同时提供了有意义的不确定性估计。