This paper provides a comprehensive survey of Deep Active Learning (DAL), a technique that aims to achieve strong performance with fewer training samples by iteratively selecting and labeling informative samples from an unlabeled dataset. The survey covers the latest advancements and new frontiers in DAL, including its definition, influential baselines, and widely used datasets. It systematically categorizes DAL methods into five perspectives: annotation types, query strategies, deep model architectures, learning paradigms, and training processes. The paper also reviews the main applications of DAL in Natural Language Processing (NLP), Computer Vision (CV), and Data Mining (DM). Finally, it discusses the challenges and future directions in DAL, highlighting the need for more efficient and effective sample selection strategies, especially in large-scale pre-trained models and generative tasks. The survey aims to serve as a valuable resource for researchers and practitioners in the field of DAL, providing insights into its methodologies, applications, and potential solutions to emerging challenges.This paper provides a comprehensive survey of Deep Active Learning (DAL), a technique that aims to achieve strong performance with fewer training samples by iteratively selecting and labeling informative samples from an unlabeled dataset. The survey covers the latest advancements and new frontiers in DAL, including its definition, influential baselines, and widely used datasets. It systematically categorizes DAL methods into five perspectives: annotation types, query strategies, deep model architectures, learning paradigms, and training processes. The paper also reviews the main applications of DAL in Natural Language Processing (NLP), Computer Vision (CV), and Data Mining (DM). Finally, it discusses the challenges and future directions in DAL, highlighting the need for more efficient and effective sample selection strategies, especially in large-scale pre-trained models and generative tasks. The survey aims to serve as a valuable resource for researchers and practitioners in the field of DAL, providing insights into its methodologies, applications, and potential solutions to emerging challenges.