Fetal ultrasound (US) data is often limited due to privacy and regulatory restrictions, posing challenges for training deep learning (DL) models. While semi-supervised learning (SSL) is commonly used for fetal US image analysis, existing SSL methods typically rely on random limited selection, which can lead to suboptimal model performance by overfitting to homogeneous labeled data. To address this, we propose a two-stage Active Learning (AL) sampler, Entropy-Guided Agreement-Diversity (EGAD), for fetal head segmentation. Our method first selects the most uncertain samples using predictive entropy, and then refines the final selection using the agreement-diversity score combining cosine similarity and mutual information. Additionally, our SSL framework employs a consistency learning strategy with feature downsampling to further enhance segmentation performance. In experiments, SSL-EGAD achieves an average Dice score of 94.57\% and 96.32\% on two public datasets for fetal head segmentation, using 5\% and 10\% labeled data for training, respectively. Our method outperforms current SSL models and showcases consistent robustness across diverse pregnancy stage data. The code is available on \href{https://github.com/13204942/Semi-supervised-EGAD}{GitHub}.

翻译：由于隐私和监管限制，胎儿超声数据通常较为有限，这给深度学习模型的训练带来了挑战。虽然半监督学习常用于胎儿超声图像分析，但现有的半监督学习方法通常依赖于随机有限选择，这可能导致模型因过拟合于同质的标注数据而性能欠佳。为解决此问题，我们提出了一种用于胎儿头部分割的两阶段主动学习采样器——熵引导的一致性-多样性。我们的方法首先使用预测熵选择最不确定的样本，然后结合余弦相似度和互信息的一致性-多样性得分来细化最终选择。此外，我们的半监督学习框架采用特征下采样的一致性学习策略，以进一步提升分割性能。在实验中，使用5%和10%的标注数据进行训练时，SSL-EGAD在两个公开的胎儿头部分割数据集上分别取得了平均Dice分数94.57%和96.32%的结果。我们的方法优于当前的半监督学习模型，并在不同孕周数据上展现出一致的鲁棒性。代码发布于\href{https://github.com/13204942/Semi-supervised-EGAD}{GitHub}。