  Pool-based active learning (AL) aims to optimize the annotation process
(i.e., labeling) as the acquisition of annotations is often time-consuming and
therefore expensive. For this purpose, an AL strategy queries annotations
intelligently from annotators to train a high-performance classification model
at a low annotation cost. Traditional AL strategies operate in an idealized
framework. They assume a single, omniscient annotator who never gets tired and
charges uniformly regardless of query difficulty. However, in real-world
applications, we often face human annotators, e.g., crowd or in-house workers,
who make annotation mistakes and can be reluctant to respond if tired or faced
with complex queries. Recently, a wide range of novel AL strategies has been
proposed to address these issues. They differ in at least one of the following
three central aspects from traditional AL: (1) They explicitly consider
(multiple) human annotators whose performances can be affected by various
factors, such as missing expertise. (2) They generalize the interaction with
human annotators by considering different query and annotation types, such as
asking an annotator for feedback on an inferred classification rule. (3) They
take more complex cost schemes regarding annotations and misclassifications
into account. This survey provides an overview of these AL strategies and
refers to them as real-world AL. Therefore, we introduce a general real-world
AL strategy as part of a learning cycle and use its elements, e.g., the query
and annotator selection algorithm, to categorize about 60 real-world AL
strategies. Finally, we outline possible directions for future research in the
field of AL.
