  Given a limited labeling budget, active learning (AL) aims to sample the most
informative instances from an unlabeled pool to acquire labels for subsequent
model training. To achieve this, AL typically measures the informativeness of
unlabeled instances based on uncertainty and diversity. However, it does not
consider erroneous instances with their neighborhood error density, which have
great potential to improve the model performance. To address this limitation,
we propose $REAL$, a novel approach to select data instances with
$\underline{R}$epresentative $\underline{E}$rrors for $\underline{A}$ctive
$\underline{L}$earning. It identifies minority predictions as \emph{pseudo
errors} within a cluster and allocates an adaptive sampling budget for the
cluster based on estimated error density. Extensive experiments on five text
classification datasets demonstrate that $REAL$ consistently outperforms all
best-performing baselines regarding accuracy and F1-macro scores across a wide
range of hyperparameter settings. Our analysis also shows that $REAL$ selects
the most representative pseudo errors that match the distribution of
ground-truth errors along the decision boundary. Our code is publicly available
at https://github.com/withchencheng/ECML_PKDD_23_Real.
