Automatic pulmonary airway segmentation from thoracic computed tomography (CT) is an essential step for the diagnosis and interventional surgical treatment of pulmonary disease. While deep learning algorithms have shown promising results in segmenting the main and larger bronchi, segmentation of the distal small bronchi remains challenging due to their limited size and divergent spatial distribution. The study aims to address the challenges associated with segmenting the pulmonary airway, particularly focusing on the distal small bronchi. Specifically, we aim to improve the accuracy and completeness of airway segmentation by developing a novel deep-learning model. To achieve this purpose, we propose an Uncertainty-based Double Attention Detail Supplement Network (UDADS-Net) to identify and supply these missing details of the airway. We introduce the Uncertainty-based Double Attention Module (UDA), which utilizes the uncertainty-based attention module to obtain the regions with high uncertainty and utilizes another attention module to identify the missing details. Moreover, we also propose the Adaptive Multi-scale Module (AMS) to optimize the process of extracting details. Evaluation of our method on the ATM’2022 airway segmentation dataset demonstrates its effectiveness, especially for segmenting distal small bronchi. Our method significantly reduces missing and fragmented parts, leading to more accurate and complete airway segmentation, and achieving higher evaluation metrics compared to the state-of-the-art (SOTA) methods.