This is just fleshing out some of the themes from Adam Zalcman's answer, which I have already accepted.
The standard proof that $ B_j\geq A_j $ for a projection $ H $ uses the spectral theorem to write
$$
H=\sum_{a} |a><a|
$$
where $ \{ |a> \} $ is a basis for the codespace. From there one shows that
$$
A_j=\frac{1}{K^2} \sum_{E \in \mathcal{E}_j } \Bigg| \sum_{a} <a|E|a> \Bigg|^2
$$
and
$$
B_j=\frac{1}{K} \sum_{E \in \mathcal{E}_j } \sum_{a,b} \Bigg|<a|E|b> \Bigg|^2
$$
then shows that $ A_j \leq B_j $ using Cauchy Schwarz for the $ K^2 $ length vectors $ <a|E|b> $ and $ \frac{1}{K}\delta_{ab} $.
If $ H $ is not a projection but merely a generic Hermitian operator then we can again use spectral theorem to write
$$
H=\sum_{a} a|a><a|
$$
where $ \{ |a> \} $ is an orthonormal eigenbasis for $ H $.
From there one shows that
$$
A_j=\frac{1}{K^2} \sum_{E \in \mathcal{E}_j } |a|^2\Bigg| \sum_{a} <a|E|a> \Bigg|^2
$$
and
$$
B_j=\frac{1}{K} \sum_{E \in \mathcal{E}_j } \sum_{a,b} ab\Bigg|<a|E|b> \Bigg|^2
$$
if $ H $ is positive semidefinite then we can still make the proof of $ A_j \leq B_j $ work using Cauchy Schwarz for the $ K^2 $ length vectors $ \sqrt{a}\sqrt{b}<a|E|b> $ and $ \frac{1}{K}\delta_{ab} $. This is a related, but slightly stronger, result than Theorem 3 of https://arxiv.org/abs/quant-ph/9611001
However if $ H $ has a mix of positive and negative eigenvalues then the proof totally breaks down and not only do we not have $ 0 \leq A_j \leq B_j $ we may even get $ B_j < 0 \leq A_j $ as in the example from Adam Zalcman above.
Indeed looking back at the example from Adam Zalcman we see that $ H $ has eigenvalues $ \epsilon \pm 1 $. So for $ 0< \epsilon <1 $ we indeed have negative eigenvalues. And for $ \epsilon=\frac{1}{2} $ it is already clear that $ B_1<0\leq A_1 $.