We propose a meta-learning method to improve the anomaly detection performance on unseen target tasks that have only unlabeled data. Existing meta-learning methods for anomaly detection have shown remarkable performance but require labeled data in target tasks. Although they can treat unlabeled data as normal assuming anomalies in the unlabeled data are negligible, this assumption is often violated in practice. As a result, the methods have low performance. Our method meta-learns with related tasks that have labeled and unlabeled data such that the expected test anomaly detection performance is directly improved when the anomaly detector is adapted to given unlabeled data. Our method is based on autoencoders (AEs), which are widely used neural network-based anomaly detectors. We model anomalous attributes for each unlabeled instance in the reconstruction loss of the AE, which are used to prevent the anomalies from being reconstructed; they can remove the effect of the anomalies. We formulate adaptation to the unlabeled data as a learning problem of the last layer of the AE and the anomalous attributes. This formulation enables the optimum solution to be obtained with a closed-form alternate update formula, which is preferable to efficiently maximize the expected test anomaly detection performance. The effectiveness of our method is experimentally shown with four real-world datasets.